In Utero Exposure to Benzene Disrupts Fetal Hematopoietic Progenitor Cell Growth via Reactive Oxygen Species
Bibliographic record
Abstract
It is hypothesized that the increasing incidence of childhood leukemia may be due to in utero exposure to environmental pollutants, such as benzene, but the mechanisms involved remain unknown. We hypothesize that reactive oxygen species (ROS) contribute to the deregulation of fetal hematopoiesis caused by in utero benzene exposure. To evaluate this hypothesis, pregnant C57Bl/6N mice were exposed to benzene or polyethylene glycol-conjugated catalase (PEG-catalase) (antioxidative enzyme) and benzene. Colony formation assays on fetal liver cells were performed to measure erythroid and myeloid progenitor cell growth potential. The presence of ROS in CD117(+) fetal liver cells was measured by flow cytometric analysis. Oxidative cellular damage was assessed by Western blot analysis of 4-hydroxynonenol (4-HNE) and nitrotyrosine products, as well as reduced to oxidized glutathione ratios. Alterations in the redox-sensitive signaling pathway nuclear factor-kappa-light-chain-enhancer of activated B cells (NF-kappaB) were measured by Western blot analysis of Inhibitor of NF-kB-alpha (IkappaB-alpha) protein levels in fetal liver tissue. In utero exposure to benzene caused a significant increase in ROS production and significantly altered fetal liver erythroid and myeloid colony numbers but did not increase the levels of 4-HNE or nitrotyrosine products or alter reduced to oxidized glutathione ratios. However, in utero exposure to benzene did cause a significant decrease in fetal liver IkappaB-alpha protein levels, suggesting activation of the NF-kappaB pathway. Benzene-induced ROS formation, abnormal colony growth, and decreased IkappaB-alpha levels were all abrogated by pretreatment with PEG-catalase. These results suggest that ROS play a key role in the development of in utero-initiated benzene toxicity potentially through disruption of hematopoietic cell signaling pathways.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".